[wip] Update

ljvmiranda921 · Aug 8, 2024 · 64b2306 · 64b2306
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diff --git a/models/v0.1.0-gliner/README.md b/models/v0.1.0-gliner/README.md
@@ -4,7 +4,7 @@
 
 This is a spaCy project that trains and evaluates new v0.1.0-gliner models.
 [GliNER](https://github.com/urchade/GLiNER) (Generalist and Lightweight Model for Named Entity Recognition) is a powerful model capable of identifying any entity type using a BERT-like encoder.
-In this project, we finetune the GliNER model with the TLUnified-NER training dataset.
+In this project, we finetune the GliNER model using the TLUnified-NER dataset.
 
 To replicate training, first you need to install the required dependencies:
 
@@ -32,7 +32,7 @@ To perform evals, run the `eval-gliner` workflow while passing the size:
 python -m spacy project run eval-gliner . --vars.size small
 ```
 
-This will evaluate on TLUnified-NER's test set ([Miranda, 2023](https://aclanthology.org/2023.sealp-1.2.pdf) and the Tagalog subsets of
+This will evaluate on TLUnified-NER's test set ([Miranda, 2023](https://aclanthology.org/2023.sealp-1.2.pdf)) and the Tagalog subsets of
 Universal NER ([Mayhew et al., 2024](https://aclanthology.org/2024.naacl-long.243/)).
 
 The evaluation results are shown in the table below:

diff --git a/models/v0.1.0-gliner/project.yml b/models/v0.1.0-gliner/project.yml
@@ -2,7 +2,7 @@ title: "Release v0.1.0-gliner"
 description: |
   This is a spaCy project that trains and evaluates new v0.1.0-gliner models.
   [GliNER](https://github.com/urchade/GLiNER) (Generalist and Lightweight Model for Named Entity Recognition) is a powerful model capable of identifying any entity type using a BERT-like encoder.
-  In this project, we finetune the GliNER model with the TLUnified-NER training dataset.
+  In this project, we finetune the GliNER model using the TLUnified-NER dataset.
 
   To replicate training, first you need to install the required dependencies:
 
@@ -30,7 +30,7 @@ description: |
   python -m spacy project run eval-gliner . --vars.size small
   ```
 
-  This will evaluate on TLUnified-NER's test set ([Miranda, 2023](https://aclanthology.org/2023.sealp-1.2.pdf) and the Tagalog subsets of
+  This will evaluate on TLUnified-NER's test set ([Miranda, 2023](https://aclanthology.org/2023.sealp-1.2.pdf)) and the Tagalog subsets of
   Universal NER ([Mayhew et al., 2024](https://aclanthology.org/2024.naacl-long.243/)).
 
   The evaluation results are shown in the table below: